code: def test_clone_pipeline():
      obj = (
          preprocessing.StandardScaler()
          | linear_model.LinearRegression(l2=42)
      )
      obj.learn_one({"x": 3}, 6)
  
      params = {"LinearRegression": {"l2": 21}}
      new = obj.clone(params)
      assert new["LinearRegression"].l2 == 21
      assert obj["LinearRegression"].l2 == 42
      assert new["LinearRegression"].weights == {}
      assert (
          new["LinearRegression"].weights
          != obj["LinearRegression"].weights
      )
  
file_path: river/river/base/test_base.py
imports: from __future__ import annotations
  from river import compose, datasets, linear_model, optim, preprocessing, stats, time_series
label_desc: Use image node_id 3 for calling a global function with example usage: test_clone_pipeline() without return types